Phase ordering with automatic window selection (PAWS): a novel motion-resistant technique for 3D coronary imaging

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Signal Processing

Background:

• Navigator acceptance imaging methods are limited by scan efficiency losses due to subject breathing pattern variations.
• Existing techniques like diminishing variance algorithm (DVA) are less influenced by breathing changes but may not fully optimize image quality.
• Phase ordering and weighting improve image quality but still rely on acceptance windows, introducing limitations.

Purpose of the Study:

• To present a novel technique for navigator acceptance imaging that is resistant to breathing variations.
• To enable effective motion artifact reduction using phase ordering within optimal scan times.
• To demonstrate the feasibility and efficacy of an automatic window-selection technique.

Main Methods:

• Development of a technique resistant to breathing variations, incorporating phase ordering for motion artifact reduction.
• Utilizing an automatic window-selection approach, eliminating the need for predefined acceptance windows.
• Validation through in vitro experiments to demonstrate feasibility and in vivo studies for efficacy.

Main Results:

• In vitro results demonstrated the feasibility of the proposed automatic window-selection technique.
• In vivo studies showed significant improvement in image quality compared to the diminishing variance algorithm (p < 0.01).
• The new technique also showed significant improvement over hybrid-ordered phase encoding methods (p < 0.05).

Conclusions:

• The presented technique offers a robust solution for motion artifact reduction in navigator acceptance imaging.
• It overcomes limitations of existing methods by being resistant to breathing pattern changes and optimizing scan time.
• This method significantly enhances image quality in dynamic imaging scenarios.